放射学教育中的人工智能:评估多项选择题难度和歧视
Emre Emekli1, Betül Nalan Karahan1
1Department of Radiology, Eskişehir Osmangazi University, Faculty of Medicine, Eskişehir, Türkiye.
Journal of medical imaging and radiation sciences
|March 29, 2025
概括
人工智能 (AI) 可以生成多选题 (MCQ),与人类专家为放射学教育所做的相似. 虽然人工智能生成的MCQ显示出希望,但完全整合需要在歧视和难度调整方面略有改进.
科学领域:
- 医学教育 医学教育
- 教育中的人工智能
背景情况:
- 高质量的多选择题 (MCQ) 对于健康教育评估至关重要.
- 手动创建MCQ是耗时的,需要专业知识.
- 大型语言模型 (LLM) 提供了自动化MCQ生成的潜力.
研究的目的:
- 为了比较人工智能生成与教师授权的MCQ在放射学教育中的有效性.
- 根据难度和歧视指数,评估人工智能生成的MCQ质量.
- 解决了解人工智能在健康教育评估中的作用的差距.
主要方法:
- 一项涉及土耳其56名X光学学生的定量研究.
- 创建了两个30个问题MCQ考试:一个由ChatGPT-4o,一个由教师成员.
- 检查涵盖了放射解剖学和定位;难度级别被分类. 分析了学生的表现和反.
主要成果:
- 人工智能生成和人类专家考试的平均分数相似 (p=0.089).
- 在考试成绩之间发现了中度的正相关性 (r=0.628).
- 与人类专家 (86.67%) 相比,人工智能问题的歧视指数略低 (73.33%).
结论:
- 法律学士可以产生与人类专家创建的质量相当的MCQ.
- 人工智能生成的MCQ显示出在健康教育中补充评估的潜力.
- 为了实现最佳的难度和歧视对齐,需要进一步精细化.
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